A Survey on Change Detection Techniques in Document Images

TL;DR

This survey reviews core change detection techniques in document images, focusing on content-based and layout-based methods, summarizing datasets, evaluation metrics, challenges, and future research directions.

Contribution

It provides a comprehensive overview of existing change detection methods in document images, categorizing techniques and highlighting research gaps.

Findings

Content-based methods analyze image content for differences.

Layout-based methods use structural information to detect changes.

The survey summarizes datasets and evaluation metrics.

Abstract

The problem of change detection in images finds application in different domains like diagnosis of diseases in the medical field, detecting growth patterns of cities through remote sensing, and finding changes in legal documents and contracts. However, this paper presents a survey on core techniques and rules to detect changes in different versions of a document image. Our discussions on change detection focus on two categories -- content-based and layout-based. The content-based techniques intelligently extract and analyze the image contents (text or non-text) to show the possible differences, whereas the layout-based techniques use structural information to predict document changes. We also summarize the existing datasets and evaluation metrics used in change detection experiments. The shortcomings and challenges the existing methods face are reported, along with some pointers for future research work.